Comparison between the ages (after controlling for type and gender)¶

Questions

What are the differences between the ages?
Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w?
Are they the same? Is there a gradient?

Age effect - General Questions

What are the differences between the ages?
Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w? Are they the same? Is there a gradient?
Are they different for the two genders?
Are they different for the two types?

Loads¶

Libraries and functions¶

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”

==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data¶

Stats¶

Differentially expressed genes¶

  52w VS 8w 104w VS 52w  104w VS 8w 
  0.3036625   0.2570053   0.3956767

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Counts¶

Genes that are differentially expressed in 52W vs 8W with abs(FC) > 2

Genes that are differentially expressed in 104W vs 8W with abs(FC) > 2

Genes that are differentially expressed in 104W vs 52W with abs(FC) > 2

Compare the numbers¶

Differentially expressed genes

Differentially more expressed genes

Differentially less expressed genes

Compare the intersections in term of expressions¶

Genes that are differentially expressed in 52W vs 8W and in 104W vs 8W (and not in 104w vs 52w)¶

With abs(FC) > 1

Genes that are differentially expressed in 52W vs 8W and in 104W vs 52W (and not in 104w vs 52w)¶

With abs(FC) > 1

Genes that are differentially expressed in 104W vs 8W and in 104W vs 52W (and not in 52w vs 8w)¶

With abs(FC) > 1

Genes that are differentially expressed in 52W vs 8W, in 104W vs 8W and in 104W vs 52W¶

With abs(FC) > 1

DEG into gene co-expression network¶

White: up-regulated
Black: down-regulated

Order: 52w VS 8w, 104w VS 52w, 104w VS 8w

GO analysis¶

Biological process¶

Dot-plot with the most over-represented GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

52w VS 8w¶

GO Tree at "../results/dge/age-effect/age/go/52w_VS_8w.png"

104w VS 52w¶

GO Tree at "../results/dge/age-effect/age/go/104w_VS_52w.png"

104w VS 8w¶

GO Tree at "../results/dge/age-effect/age/go/104w_VS_8w.png"

Cellular components¶

Dot-plot with the most over-represented GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Molecular functions¶

Dot-plot with the most over-represented GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways¶

Error in `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", : replacement has 34 rows, data has 48
Traceback:

1. plot_kegg_pathways(age_deg$over_represented_KEGG[, "category"], 
 .     age_deg$fc_deg, "../results/dge/age-effect/age/kegg/over_repr_kegg/")
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, labels, value = c("", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

Pathway graphs available at ../results/dge/age-effect/age/over_repr_kegg/

Pathway graphs available at ../results/dge/age-effect/age/under_repr_kegg/

	Wald padj < 0.05	LFC > 0 (Wald padj < 0.05)	LFC < 0 (Wald padj < 0.05)
52w VS 8w	2157	1176	981
104w VS 52w	2284	1252	1032
104w VS 8w	3192	1701	1491